MEMS modeling system and method

ABSTRACT

A system and method for modeling microelectromechanical devices is disclosed. An embodiment includes separating the microelectromechanical design into separate regions and modeling the separate regions separately. Parametric parameters or parametric equations may be utilized in the separate models. The separate models may be integrated into a MEMS device model. The MEMS device model may be tested and calibrated, and then may be used to model new designs for microelectromechanical devices.

TECHNICAL FIELD

Embodiments relate generally to a system and method for modeling devicesand, more particularly, to a system and method for modelingmicroelectromechanical devices.

BACKGROUND

Generally, when a behavior model for microelectromechanical devices isdeveloped, developers may take one of two approaches: a “top-down”approach or a “bottom-up” approach. In a “top-down” approach thedeveloper begins with desired features of the device and then attemptsto determine a geometry and shape of the device that will provide thedesired features, resulting in an eventual physical device that may betested. However, in this approach, if the result produces a physicalmicroelectromechanical device that is outside of the desiredspecifications (and, therefore, is not suitable), the entire process,from the design to the physical device, must be redone, losing anyincremental progress that may have been achieved.

Conversely, in a “bottom-up” approach, a physical three-dimensionalstructure of the desired microelectromechanical device is built first.Once built, the microelectromechanical structure may undergo afinite-element method or a boundary-element method analysis, which maythen be transformed into a behavioral model of themicroelectromechanical structure. From this behavior model furtherdesign may be performed for new microelectromechanical devices. However,similar to the “top-down” approach described above, if amicroelectromechanical device is designed from the behavior model and isoutside of the desired specifications, the entire process must berepeated, including the original manufacturing of the three-dimensionalstructure, the finite-element method, the transformation into a behaviormodel, and the eventual redesign of the desired device. As such, neitherof these approaches allows for an improvement of the behavioral modelwithout again undertaking the entire design process, thereby wastingtime and resources.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the embodiments, and the advantagesthereof, reference is now made to the following descriptions taken inconjunction with the accompanying drawings, in which:

FIG. 1 illustrates a first dummy microelectromechanical (MEMS) device inaccordance with an embodiment;

FIGS. 2A-2B illustrates the formation of a MEMS behavior model inaccordance with an embodiment; and

FIG. 3 illustrates a usage of the MEMS behavior model in accordance withan embodiment.

Corresponding numerals and symbols in the different figures generallyrefer to corresponding parts unless otherwise indicated. The figures aredrawn to clearly illustrate the relevant aspects of the preferredembodiments and are not necessarily drawn to scale.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The making and using of the presently preferred embodiments arediscussed in detail below. It should be appreciated, however, that theembodiments provide many applicable inventive concepts that can beembodied in a wide variety of specific contexts. The specificembodiments discussed are merely illustrative of specific ways to makeand use the embodiments, and do not limit the scope of the embodiments.

Embodiments will be described with respect to a specific context, namelya system and method for modeling microelectromechanical devices. Otherembodiments may also be applied, however, to other modeling systems.

With reference to FIG. 1, there is shown a first dummymicroelectromechanical (MEMS) design 100. The first dummy MEMS design100 may be, for example, a dummy MEMS accelerometer that comprises amoveable mass 101, springs 103, and electrodes 105. The dummy MEMSaccelerometer may be designed so that, if it were ever to bemanufactured and used in operation, the moveable mass 101 would move inrelation to the overall motion of the accelerometer, thereby causing thecapacitance of the electrodes 105 to change and allowing a processor(not shown in FIG. 1) to determine the acceleration from the movement ofthe movable mass 101. The springs 103 would attach the moveable mass 101to the overall structure while still allowing the moveable mass 101 tomove.

The first dummy MEMS design 100 is referred to as a “dummy” devicebecause it is not the final product that will be manufactured forcommerce. Rather, it is a design which is intended to be used to helpgenerate a final design for manufacture. However, as one of ordinaryskill in the art will recognize, this description of a “dummy” device isnot meant to be limiting, and any other suitable design may also beutilized. For example, a final product design that was previously builtmay also be used if available, and other types of MEMS device designs,such as gyroscopes, resonators and micro-mirrors, combinations of these,or the like, may alternatively be utilized. Any design, no matter itsorigin, may alternatively be used as the first dummy MEMS design 100.

The first dummy MEMS design 100 may be stored, e.g., within a maskdatabase (represented in FIG. 1 by dashed box 113). The mask database113 may be a two dimensional mask database that stores a series ofphotolithographic masks that may be used in the manufacture of MEMSdevices, such as the MEMS accelerometer. However, the described maskdatabase is not intended to limit how the first dummy MEMS design 100may be stored, as any suitable storage medium, such as a threedimensional mask database or other design storage, may alternatively beutilized.

The first dummy MEMS design 100 may, if desired, be manufactured using,e.g., a series of two dimensional masks from a mask database 113 alongwith a suitable manufacturing process to form a structure from the firstdummy MEMS design 100. For example, a first mask may be utilized toilluminate a first photoresist, which may then be utilized to protect orexpose desired regions for further processing, such as etching. By usinga series of these masks in sequence during a process, a desired shape orstructure may be formed until the overall desired first dummy MEMSdesign 100 is formed as a physical structure.

FIG. 1 also illustrates that the first dummy MEMS design 100 within,e.g., the mask database 113 may be separated into separate dummy regionsor dummy modules, such as a mass module 107, an electrode module 109,and a spring module 111. As their names suggest, the mass module 107 mayinclude that portion of the first dummy MEMS design 100 that includesthe moveable mass 101, the spring module 111 may include that portion ofthe first dummy MEMS design 100 that includes the springs 103 of thefirst dummy MEMS design 100, and the electrode module 109 may includesthat portion of the first dummy MEMS design 100 that contains theelectrodes 105 of the first dummy MEMS design 100.

The separate dummy regions may be stored as separate regions, or dummylayers, of a two-dimensional mask within the two dimensional maskdatabase, and each of the separate dummy regions may be maintainedwithin the two dimensional mask database until the separate dummyregions are joined into a single mask. For example, when the first dummyMEMS design 100 is desired to be finished and/or manufactured, theseparate dummy regions, such as the mass module 107, the electrodemodule 109, and the spring module 111, may be joined together through,e.g., an “AND” process in order to form a single mask for the firstdummy MEMS design 100.

After the first dummy MEMS design 100 has been separated into theseparate dummy regions, each of the separate dummy regions may bemodeled separately from each other. For example, the electrode module109 may be modeled separately from the mass module 107, which may bemodeled separately from the spring module 111. As an example only, theparameters of the electrode module 109, such as its sensor capacitanceor its electrostatic forces, may be parametrically modeled based uponthe geometry and shape of the electrodes 105 in the first dummy MEMSdesign 100. For instance, the parameters of the electrodes 105 in thefirst dummy MEMS design 100 may be used to satisfy an equation, such asa parametric equation, related to the electrostatic force of theelectrodes, such as Equation 1:

$\begin{matrix}{F_{e} = {\frac{\in_{0}{LH}}{2D^{2}}\left( {V_{m} - V_{s}} \right)^{2}}} & {{Eq}.\mspace{14mu} 1}\end{matrix}$

-   -   Where:        -   F_(e)=Electrostatic Force        -   ε_(o)=Permittivity of Free Space        -   L=Length of the Electrodes        -   H=Height of the Electrodes        -   D=Distance between the Electrodes        -   V_(m)=Main Electrode Voltage        -   V_(s)=Secondary Electrode Voltage            In this parametric equation the length (L) of the electrodes            105, the height (H) of the electrodes 105, and the            distance (D) of the electrodes 105 may be parametric            parameters that may be utilized in the parametric modeling            of future designs.

Such parametric equations as the one illustrated above with respect toEquation 1 allow certain variables, known as parametric variables, torepresent a much more complicated relationship. In the example ofEquation 1 above, the electrostatic force may be represented by Equation1 using the parametric variables L, H and D, although many morevariables may be used to calculate the electrostatic force. However, byutilizing the parametric equation for the electrostatic force, a changein one or more of the parametric variables represents a change thatwould occur in the relation between that variable and the resultingchange in the electrostatic force. As such, complicated equations may besimplified for purposes of modeling.

However, as one of ordinary skill in the art will recognize, theparametric equations presented are not the only equations that may beutilized in order to obtain models for the mass module 107, the springmodule 111, or the electrode module 109. Other equations, such asnon-parametric equations or parametric equations with differentparametric parameters than those listed above in Equation 1, mayalternatively be utilized. Any suitable equation or combination ofequations may alternatively be utilized to model the mass module 107,the spring module 111, or the electrode module 109, and all suchequations are fully intended to be included within the scope of theembodiments.

Additionally, the parameters of the electrodes 105 in the first dummyMEMS design 100 may also be used to satisfy an equation related to thesensor capacitance of the electrodes 105, such as the parametricequation illustrated in Equation 2:

$\begin{matrix}{C_{s} = \frac{\in_{0}{LH}}{2D}} & {{Eq}.\mspace{14mu} 2}\end{matrix}$

-   -   Where:        -   C_(s)=Sensor Capacitance        -   ε_(o)=Permittivity of Free Space        -   L=Length of the Electrodes        -   H=Height of the Electrodes        -   D=Distance between the Electrodes            Similar to Equation 1 discussed above, in Equation 2 the            length (L) of the electrodes 105, the height (H) of the            electrodes 105, and the distance (D) of the electrodes 105            may be parametric parameters.

Optionally, the initial parametric parameters of Equation 1 and Equation2 may be constrained using, e.g., the design rules for the manufacturingprocesses being utilized. For example, if a 90 nm process node design isbeing utilized for the manufacturing of the eventual product MEMSdevice, 90 nm process node design rules may be utilized to furtherconstrain the parameters for the equations, such as constraining thedistance between electrodes “D” to be 1 μm<D<2 μm.

After the equations illustrated by Equation 1 and Equation 2 have beencreated for the electrode module 109, similar equations may be createdfor the mass module 107 and the spring module 111. As an example only,the mass module 107 may be modeled using an equation similar to Equation3:F_(mass) _(—) _(force)=MA  Eq. 3

-   -   Where:        -   F_(mass) _(—) _(force)=Force on the Mass Module        -   M=Mass of the Mass Module        -   A=Acceleration of the Mass Module            Similarly, the spring module 111 may be modeled using an            equation similar to Equation 4:            F_(spring) _(—) _(force)=KX  Eq. 4    -   Where:        -   F_(spring) _(—) _(force)=Force of the Springs        -   K=Spring Constant of the Spring        -   X=Displacement of the Spring

As one of ordinary skill in the art will recognize, the specificequations illustrated above are merely illustrative of embodiments, andare not meant to limit the embodiments in any manner. Any equations thatmay be suitable to model specific regions of microelectromechanicaldevices, such as equations for other geometries, shapes, or even otherdevices such like gyroscopes and resonators, may be alternativelyutilized. All such equations are fully intended to be included withinthe scope of the present embodiments.

FIG. 2A illustrates the combining of each of the separate dummy regionsinto a MEMS behavior model 201. In an embodiment, once the spring module111, the mass module 107, and the electrode module 109 have been modeled(using, e.g., Equations 1-4 described above), these models areintegrated into the MEMS behavior model 201 so that these equations canbe utilized to design other MEMS devices, such as final products foreventual mass production.

FIG. 2B illustrates a block diagram of a processing system 200 that maybe used to implement the MEMS behavior model 201. The processing system200 is a general purpose computer platform and may be used to implementany or all of the processes discussed herein. The processing system 200may comprise a processing unit 204, such as a desktop computer, aworkstation, a laptop computer, or a dedicated unit customized for aparticular application. The processing system 200 may be equipped with adisplay 203 and one or more input/output devices 205, such as a mouse, akeyboard, or printer. The processing unit 204 may include a centralprocessing unit (CPU) 206, memory 208, a mass storage device 210, avideo adapter 214, and an I/O interface 216 connected to a bus 212.

The bus 212 may be one or more of any type of several bus architecturesincluding a memory bus or memory controller, a peripheral bus, or videobus. The CPU 206 may comprise any type of electronic data processor, andthe memory 208 may comprise any type of system memory, such as staticrandom access memory (SRAM), dynamic random access memory (DRAM), orread-only memory (ROM).

The mass storage device 210 may comprise any type of storage deviceconfigured to store data, programs, and other information and to makethe data, programs, and other information accessible via the bus 212.The mass storage device 210 may comprise, for example, one or more of ahard disk drive, a magnetic disk drive, or an optical disk drive.

The video adapter 214 and the I/O interface 216 provide interfaces tocouple external input and output devices to the processing unit 204. Asillustrated in FIG. 2B, examples of input and output devices include thedisplay 203 coupled to the video adapter 214 and the I/O device 205,such as a mouse, keyboard, printer, and the like, coupled to the I/Ointerface 216. Other devices may be coupled to the processing unit 204,and additional or fewer interface cards may be utilized. For example, aserial interface card (not shown) may be used to provide a serialinterface for a printer. The processing unit 204 also may include anetwork interface 218 that may be a wired link to a local area network(LAN) or a wide area network (WAN) 220 and/or a wireless link.

It should be noted that the processing system 200 may include othercomponents. For example, the processing system 200 may include powersupplies, cables, a motherboard, removable storage media, cases, and thelike. These other components, although not shown, are considered part ofthe processing system 200.

Embodiments of the MEMS behavior model 201 (shown in FIG. 2A) areimplemented on the processing system 200, such as by program codeexecuted by the CPU 206. For instance, the MEMS behavior model 201 maybe input into the processing system 200 by inputting the models (e.g.,Equations 1-4 above) for each of the dummy regions (e.g., the springmodule 111, the mass module 107, and the electrode module 109) using,e.g., I/O component 205. Additionally, the MEMS behavior model 201 maybe stored in the mass storage device 210. When desired, a user (notshown) may use the CPU 206 and the memory 208 to implement the MEMSbehavior model 201.

FIG. 3 illustrates that once the MEMS behavior model 201 has beenpopulated and input in to the processing system 200, it may becalibrated using a calibration design 301. In an embodiment thecalibration design 301 may be generated by a user (not shown) thatutilizes the MEMS behavior model 201 (with the integrated models fromthe spring module 111, the mass module 107, and the electrode module109) to formulate the calibration design 301. Such design work mayinclude taking desired effects, such as the sensor capacitance and backcalculating the necessary physical design that would meet suchspecifications without requiring a full physical production model to beformed. The calibration design 301 may be in the shape of a desireddevice such as, e.g., an accelerometer.

Once designed using the MEMS behavior model 201, the calibration design301 may be tested in order to ensure that it meet all of the desiredspecifications for the calibration design 301. In an embodiment thecalibration design 301 may be tested while still in the design stage bytesting the calibration design 301 with other models. For example, amethod of silicon correlation, in which a spring constant (K₀) and mass(M₀) are pre-determined, may be used. These two parameters may be usedto determine the resonant frequency (f₀) of the MEMS device by Equation5:

$\begin{matrix}{f_{0} = {\frac{1}{2\pi}\sqrt{\frac{K_{0}}{M_{0}}}}} & {{Eq}.\mspace{14mu} 5}\end{matrix}$Such a resonant frequency can be measured by by spectrum analysis and anew f₀′ can be obtained to correlate the spring constant and the mass tobe K₀′ and M₀′, thereby developing a new calibrated equation:

$\begin{matrix}{f_{0}^{\prime} = {\frac{1}{2\pi}\sqrt{\frac{K_{0}^{\prime}}{M_{0}^{\prime}}}}} & {{Eq}.\mspace{14mu} 6}\end{matrix}$From this, the calibration design 301 is electronically modeled prior tomanufacturing, may be utilized in order to test the calibration design301 to ensure that the product MEMS device 301 remains within all of thedesired specifications. Any other suitable method of testing thecalibration design 301 while the calibration design 301 is in the designstage, such as a co-simulation of the calibration design 301 with anapplication-specific integrated circuit (ASIC) model, a transientsimulation to check the nonlinearity specification, or a noisesimulation to check the signal to noise ratio (SNR), combinations ofthese, or the like, may also alternatively be utilized to assess theperformance of the MEMS behavior model 201 with the integrated modelsfrom the spring module 111, the mass module 107, and the electrodemodule 109.

However, as one of ordinary skill in the art will recognize, testing thecalibration design 301 while only in a design stage is not the onlymethod suitable for calibrating the MEMS behavior model 201. In analternative embodiment, the calibration design 301 may be tested byusing the calibration design 301 to manufacture a physical product fromthe calibration design 301. Once manufactured into a physical product,the calibration design 301 may be physically tested to determine if itmeets the desired specifications. As an example only, a threedimensional model buildup and mechanical analysis, such as finiteelement (FEM), may be performed on the calibration design 301 in orderto determine if the calibration design 301 is within the desiredspecifications. Alternatively, any other suitable physical test andsimulation, such as boundary element (BEM), other numerical andanalytical solutions, combinations of these, or the like, mayalternatively be utilized.

If the calibration design 301 passes all of the tests (whether they bemodeling tests, physical tests, or some combination of these) andremains within the desired specifications, the MEMS behavior model 201may be ready for use in order to design further MEMS devices. However,if the calibration design 301 fails to meet any of the specifications,then the MEMS behavior model 201 may be adjusted and calibrated in orderto ensure that the MEMS behavior model 201 produces a product that iswithin the desired specifications. For example, a user may adjust one ormore of the parametric parameters in the models of the mass module 107,the spring module 111, and/or the electrode module 109 may be adjustedin order to adjust the behavior models. Such an adjustment of theparametric parameters within the models allows the MEMS behavior model201 to be updated without also requiring the manufacturing of a newfirst dummy MEMS design 100 (shown in FIG. 1 above).

For example, if the calibration design 301 is discovered to be out ofspecification, a designer may go back to the parametric equations forthe electrode module 109 and adjust one of the parametric parameters(e.g., the length of the electrodes (L), the height of the electrodes(H), or the distance of the electrodes (D) in Equation 1 above, or evenchange the number of electrodes) in order to tune the parametricequations of the model for the electrode module 109. The decision as towhich of the parameters may be tuned may be based on engineeringknowledge, experience, trial-and-error, physical trend, fabricationcapability, combinations of these, or the like

By adjusting one or more of the parametric parameters in one or more ofthe models utilized for the mass module 107, the spring module 111,and/or the electrode module 109, the MEMS behavior model 201 may beupdated and corrected without having to start the process completelyover and resort to a brand new manufactured dummy MEMS device 100. Byskipping this manufacturing step, time and effort may be reduced,thereby streamlining the time of design and making the overall designeffort more efficient.

Once the MEMS behavior model 201 has been populated and calibrated (asdiscussed above with respect to FIG. 2 and FIG. 3), the MEMS behaviormodel 201 is ready to be used by a designer. For example, a designer mayuse the MEMS behavior model 201 to produce new designs for MEMS devicesthat meet the designer's desired criteria. Once a final design has beenachieved using the MEMS behavior model 201, the final design may beimplemented in a manufacturing process to manufacture MEMS devices thatalso meet the desired criteria.

In accordance with an embodiment, a method for modelingmicroelectromechanical devices comprising separating a MEMS design intoa plurality of regions, at least one of the plurality of regions beingin a separate dummy layer than another one of the plurality of regions,and developing a model for each of the plurality of regions is provided.The model for each of the plurality of regions is entered into a MEMSmodel.

In accordance with another embodiment, a method for modelingmicroelectromechanical devices comprising partitioning athree-dimensional MEMS design into a first region and a second regionand developing a first model for the first region and a second model forthe second region, the first model being different from the second modelis provided. The first model and the second model are integrated into aMEMS device model.

In accordance with yet another embodiment, a computer program productfor modeling microelectromechanical devices is provided. The computerprogram product has a non-transitory computer readable medium with acomputer program embodied thereon, the computer program comprisingcomputer program code for receiving a first model and a second model,the first model comprising a first region model of a first region of amicroelectromechanical device and the second model comprising a secondregion model of a second region of the microelectromechanical device.The computer program also comprises computer program code for deriving aMEMS model from the first model and the second model.

Although the embodiments and their advantages have been described indetail, it should be understood that various changes, substitutions andalterations can be made herein without departing from the spirit andscope of the embodiments as defined by the appended claims. For example,models that do not utilize parametric parameters may also be utilized.Additionally, the MEMS behavior model 201 may be calibrated using eithera detailed design that is either representative or else physical.

Moreover, the scope of the present application is not intended to belimited to the particular embodiments of the process, machine,manufacture, composition of matter, means, methods and steps describedin the specification. As one of ordinary skill in the art will readilyappreciate from the disclosure of the embodiments, processes, machines,manufacture, compositions of matter, means, methods, or steps, presentlyexisting or later to be developed, that perform substantially the samefunction or achieve substantially the same result as the correspondingembodiments described herein may be utilized according to theembodiments. Accordingly, the appended claims are intended to includewithin their scope such processes, machines, manufacture, compositionsof matter, means, methods, or steps.

What is claimed is:
 1. A method for modeling microelectromechanicaldevices, the method comprising: separating a MEMS design into aplurality of regions, each of the plurality of regions being structuralregions capable of being stored within a two-dimensional mask database,at least one of the plurality of regions being in a separate dummy layerthan another one of the plurality of regions; developing a model foreach of the plurality of regions; and entering the model for each of theplurality of regions into a MEMS model on a processing system.
 2. Themethod of claim 1, wherein the developing the model for each of theplurality of regions further comprises developing a parametric model foreach of the plurality of regions.
 3. The method of claim 2, wherein theparametric model for each of the plurality of regions further comprisesa parametric equation.
 4. The method of claim 1, further comprisingcalibrating the MEMS model after the entering the model for each of theplurality of regions into the MEMS model.
 5. The method of claim 4,wherein the calibrating the MEMS model is performed at least in part bysilicon correlation.
 6. The method of claim 4, wherein the calibratingthe MEMS model is performed at least in part by three dimensionalanalysis.
 7. The method of claim 4, wherein the calibrating the MEMSmodel further comprises: testing the MEMS model to determine if the MEMSmodel fails a set of specifications; and if the MEMS model fails the setof specifications, adjusting one of the models of each of the pluralityof regions.
 8. The method of claim 7, wherein the adjusting one of themodels of one of the plurality of regions further comprises adjusting atleast one parametric parameter in one of the models of each of theplurality of regions.
 9. A method for modeling microelectromechanicaldevices, the method comprising: partitioning a three-dimensional MEMSdesign into a first region and a second region, wherein the first regionis adjacent to the second region and the first region being in aseparate layer than the second region; developing a first model for thefirst region and a second model for the second region, the first modelbeing different from the second model; storing the first model and thesecond model in a two-dimensional mask database; and integrating using aprocessor the first model and the second model into a MEMS device model.10. The method of claim 9, wherein the developing a first modelcomprises developing a parametric model.
 11. The method of claim 9,wherein the first model comprises a parametric parameter.
 12. The methodof claim 9, further comprising calibrating the MEMS device model afterthe integrating the first mode and the second model into the MEMS devicemodel.
 13. The method of claim 12, wherein the calibrating the MEMSdevice model further comprises performing a silicon correlation.
 14. Themethod of claim 12, wherein the calibrating the MEMS device modelfurther comprises performing a three dimensional analysis.
 15. Themethod of claim 12, wherein the calibrating the MEMS device modelfurther comprises: testing the MEMS device model; and adjusting thefirst model if the MEMS device model fails the testing the MEMS devicemodel.
 16. The method of claim 15, wherein the adjusting the first modelfurther comprises adjusting a parametric parameter within the firstmodel.
 17. A computer program product for modelingmicroelectromechanical devices, the computer program product having anon-transitory computer readable medium with a computer program embodiedthereon, the computer program comprising: computer program code forreceiving a first model and a second model from a two-dimensional maskdatabase, the first model comprising a first region model of a firstregion of a microelectromechanical device and the second modelcomprising a second region model of a second region of themicroelectromechanical device, the first model and the second modelmodeling separate structures of the microelectromechanical device; andcomputer program code for deriving a MEMS model from the first model andthe second model.
 18. The computer program product of claim 17, whereinthe first model comprises a parametric model.
 19. The computer programproduct of claim 17, wherein the first model comprises at least oneparametric parameter.
 20. The computer program product of claim 17,further comprising computer program code for calibrating the MEMS model.